Abstract
In this work we study the detection of weak stimuli by spiking (integrate-and-fire) neurons in the presence of certain level of noisy background neural activity. Our study has focused in the realistic assumption that the synapses in the network present activity-dependent processes, such as short-term synaptic depression and facilitation. Employing mean-field techniques as well as numerical simulations, we found that there are two possible noise levels which optimize signal transmission. This new finding is in contrast with the classical theory of stochastic resonance which is able to predict only one optimal level of noise. We found that the complex interplay between adaptive neuron threshold and activity-dependent synaptic mechanisms is responsible for this new phenomenology. Our main results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic stochastic synaptic models and realistic signals such as poissonian spike trains.
Highlights
It is known that a certain level of noise can enhance the detection of weak input signals for some nonlinear systems
Using our IF neuron model with adaptive threshold, we studied the level of background noisy activity received by a postsynaptic neuron which improves its ability to detect an incoming weak signal
This signal is considered weak in the sense that, if the level of noise is zero or sufficiently low, the neuron does not generate action potential (AP) strongly correlated with the signal [31]
Summary
It is known that a certain level of noise can enhance the detection of weak input signals for some nonlinear systems. SR behavior has been extensively studied in many works, most of them assume a controlled source of noise that affects the dynamics of the system additively and, in some cases, without temporal correlations Such assumption is no longer valid in in vivo experiments in actual neural systems, where noise is the result of the inherent activity of the medium (which could be, for instance, the highly irregular spontaneous activity of other cortical regions projecting to the structure of interest) and, not controlled by the experimentalist. The effect of such stochasticity on the dynamics of a particular neuron could, involve details concerning concrete biological mechanisms not considered yet. The competition between STD and STF may be highly relevant in signal detection in noisy environments, as for instance in cortical gain control [17] or in spike coincidence detection [18], and they could have a main role in SR tasks
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